Overview

Dataset statistics

Number of variables23
Number of observations131
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory23.7 KiB
Average record size in memory185.0 B

Variable types

Numeric20
Categorical3

Alerts

Year has constant value "2014" Constant
Country has a high cardinality: 131 distinct values High cardinality
Life expectancy is highly correlated with Status and 12 other fieldsHigh correlation
Adult Mortality is highly correlated with Status and 6 other fieldsHigh correlation
infant deaths is highly correlated with Life expectancy and 5 other fieldsHigh correlation
Alcohol is highly correlated with Status and 6 other fieldsHigh correlation
percentage expenditure is highly correlated with Alcohol and 3 other fieldsHigh correlation
Hepatitis B is highly correlated with Life expectancy and 2 other fieldsHigh correlation
Measles is highly correlated with infant deaths and 5 other fieldsHigh correlation
BMI is highly correlated with Status and 5 other fieldsHigh correlation
under-five deaths is highly correlated with Life expectancy and 5 other fieldsHigh correlation
Polio is highly correlated with Life expectancy and 3 other fieldsHigh correlation
Diphtheria is highly correlated with Life expectancy and 2 other fieldsHigh correlation
HIV/AIDS is highly correlated with Life expectancy and 2 other fieldsHigh correlation
GDP is highly correlated with Alcohol and 4 other fieldsHigh correlation
thinness 1-19 years is highly correlated with infant deaths and 6 other fieldsHigh correlation
thinness 5-9 years is highly correlated with Life expectancy and 7 other fieldsHigh correlation
Income composition of resources is highly correlated with Status and 8 other fieldsHigh correlation
Schooling is highly correlated with Status and 8 other fieldsHigh correlation
Population is highly correlated with infant deaths and 4 other fieldsHigh correlation
Status is highly correlated with Life expectancy and 5 other fieldsHigh correlation
Year is highly correlated with StatusHigh correlation
Total expenditure is highly correlated with Life expectancy and 4 other fieldsHigh correlation
Country is uniformly distributed Uniform
df_index has unique values Unique
Country has unique values Unique
percentage expenditure has unique values Unique
GDP has unique values Unique
Population has unique values Unique
infant deaths has 34 (26.0%) zeros Zeros
Measles has 53 (40.5%) zeros Zeros
under-five deaths has 30 (22.9%) zeros Zeros

Reproduction

Analysis started2022-10-15 21:14:13.371024
Analysis finished2022-10-15 21:14:56.407747
Duration43.04 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct131
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1408.580153
Minimum1
Maximum2923
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-10-15T21:14:56.496827image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile121
Q1658
median1443
Q32048
95-th percentile2706.5
Maximum2923
Range2922
Interquartile range (IQR)1390

Descriptive statistics

Standard deviation835.5568751
Coefficient of variation (CV)0.5931908621
Kurtosis-1.130869609
Mean1408.580153
Median Absolute Deviation (MAD)689
Skewness0.04158390511
Sum184524
Variance698155.2916
MonotonicityStrictly increasing
2022-10-15T21:14:56.626243image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
0.8%
20561
 
0.8%
20241
 
0.8%
20081
 
0.8%
19921
 
0.8%
19761
 
0.8%
19601
 
0.8%
19431
 
0.8%
18941
 
0.8%
18781
 
0.8%
Other values (121)121
92.4%
ValueCountFrequency (%)
11
0.8%
171
0.8%
331
0.8%
491
0.8%
811
0.8%
971
0.8%
1131
0.8%
1291
0.8%
1451
0.8%
1931
0.8%
ValueCountFrequency (%)
29231
0.8%
29071
0.8%
28431
0.8%
28271
0.8%
28111
0.8%
27311
0.8%
27151
0.8%
26981
0.8%
26821
0.8%
26661
0.8%

Country
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct131
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
Afghanistan
 
1
Portugal
 
1
Philippines
 
1
Peru
 
1
Paraguay
 
1
Other values (126)
126 

Length

Max length24
Median length21
Mean length8.198473282
Min length4

Characters and Unicode

Total characters1074
Distinct characters49
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique131 ?
Unique (%)100.0%

Sample

1st rowAfghanistan
2nd rowAlbania
3rd rowAlgeria
4th rowAngola
5th rowArgentina

Common Values

ValueCountFrequency (%)
Afghanistan1
 
0.8%
Portugal1
 
0.8%
Philippines1
 
0.8%
Peru1
 
0.8%
Paraguay1
 
0.8%
Papua New Guinea1
 
0.8%
Panama1
 
0.8%
Pakistan1
 
0.8%
Nigeria1
 
0.8%
Niger1
 
0.8%
Other values (121)121
92.4%

Length

2022-10-15T21:14:56.758353image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
and3
 
2.0%
guinea3
 
2.0%
republic2
 
1.3%
afghanistan1
 
0.7%
brazil1
 
0.7%
botswana1
 
0.7%
bosnia1
 
0.7%
herzegovina1
 
0.7%
bhutan1
 
0.7%
benin1
 
0.7%
Other values (138)138
90.2%

Most occurring characters

ValueCountFrequency (%)
a172
16.0%
i101
 
9.4%
n83
 
7.7%
e74
 
6.9%
r61
 
5.7%
o59
 
5.5%
u45
 
4.2%
l40
 
3.7%
s36
 
3.4%
t35
 
3.3%
Other values (39)368
34.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter898
83.6%
Uppercase Letter152
 
14.2%
Space Separator22
 
2.0%
Dash Punctuation2
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a172
19.2%
i101
11.2%
n83
9.2%
e74
 
8.2%
r61
 
6.8%
o59
 
6.6%
u45
 
5.0%
l40
 
4.5%
s36
 
4.0%
t35
 
3.9%
Other values (16)192
21.4%
Uppercase Letter
ValueCountFrequency (%)
S14
 
9.2%
M14
 
9.2%
B13
 
8.6%
C13
 
8.6%
T11
 
7.2%
A11
 
7.2%
G11
 
7.2%
P9
 
5.9%
L9
 
5.9%
N7
 
4.6%
Other values (11)40
26.3%
Space Separator
ValueCountFrequency (%)
22
100.0%
Dash Punctuation
ValueCountFrequency (%)
-2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1050
97.8%
Common24
 
2.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a172
16.4%
i101
 
9.6%
n83
 
7.9%
e74
 
7.0%
r61
 
5.8%
o59
 
5.6%
u45
 
4.3%
l40
 
3.8%
s36
 
3.4%
t35
 
3.3%
Other values (37)344
32.8%
Common
ValueCountFrequency (%)
22
91.7%
-2
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1074
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a172
16.0%
i101
 
9.4%
n83
 
7.7%
e74
 
6.9%
r61
 
5.7%
o59
 
5.5%
u45
 
4.2%
l40
 
3.7%
s36
 
3.4%
t35
 
3.3%
Other values (39)368
34.3%

Year
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2014
131 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters524
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2014
2nd row2014
3rd row2014
4th row2014
5th row2014

Common Values

ValueCountFrequency (%)
2014131
100.0%

Length

2022-10-15T21:14:56.864532image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-15T21:14:56.954804image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
2014131
100.0%

Most occurring characters

ValueCountFrequency (%)
2131
25.0%
0131
25.0%
1131
25.0%
4131
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number524
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2131
25.0%
0131
25.0%
1131
25.0%
4131
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common524
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2131
25.0%
0131
25.0%
1131
25.0%
4131
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII524
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2131
25.0%
0131
25.0%
1131
25.0%
4131
25.0%

Status
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
Developing
112 
Developed
19 

Length

Max length10
Median length10
Mean length9.854961832
Min length9

Characters and Unicode

Total characters1291
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDeveloping
2nd rowDeveloping
3rd rowDeveloping
4th rowDeveloping
5th rowDeveloping

Common Values

ValueCountFrequency (%)
Developing112
85.5%
Developed19
 
14.5%

Length

2022-10-15T21:14:57.030352image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-15T21:14:57.126088image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
developing112
85.5%
developed19
 
14.5%

Most occurring characters

ValueCountFrequency (%)
e281
21.8%
D131
10.1%
v131
10.1%
l131
10.1%
o131
10.1%
p131
10.1%
i112
 
8.7%
n112
 
8.7%
g112
 
8.7%
d19
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1160
89.9%
Uppercase Letter131
 
10.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e281
24.2%
v131
11.3%
l131
11.3%
o131
11.3%
p131
11.3%
i112
 
9.7%
n112
 
9.7%
g112
 
9.7%
d19
 
1.6%
Uppercase Letter
ValueCountFrequency (%)
D131
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1291
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e281
21.8%
D131
10.1%
v131
10.1%
l131
10.1%
o131
10.1%
p131
10.1%
i112
 
8.7%
n112
 
8.7%
g112
 
8.7%
d19
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1291
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e281
21.8%
D131
10.1%
v131
10.1%
l131
10.1%
o131
10.1%
p131
10.1%
i112
 
8.7%
n112
 
8.7%
g112
 
8.7%
d19
 
1.5%

Life expectancy
Real number (ℝ≥0)

HIGH CORRELATION

Distinct99
Distinct (%)75.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.51984733
Minimum48.1
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-10-15T21:14:57.226717image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum48.1
5-th percentile57.15
Q164.65
median72
Q375.8
95-th percentile82.65
Maximum89
Range40.9
Interquartile range (IQR)11.15

Descriptive statistics

Standard deviation8.605225781
Coefficient of variation (CV)0.1220255872
Kurtosis-0.3709548091
Mean70.51984733
Median Absolute Deviation (MAD)5.6
Skewness-0.2243459847
Sum9238.1
Variance74.04991075
MonotonicityNot monotonic
2022-10-15T21:14:57.355918image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
74.84
 
3.1%
733
 
2.3%
74.63
 
2.3%
74.53
 
2.3%
893
 
2.3%
71.72
 
1.5%
662
 
1.5%
58.42
 
1.5%
56.72
 
1.5%
58.12
 
1.5%
Other values (89)105
80.2%
ValueCountFrequency (%)
48.11
0.8%
51.71
0.8%
52.11
0.8%
52.61
0.8%
53.61
0.8%
56.72
1.5%
57.61
0.8%
57.81
0.8%
57.91
0.8%
581
0.8%
ValueCountFrequency (%)
893
2.3%
881
 
0.8%
832
1.5%
82.71
 
0.8%
82.61
 
0.8%
82.51
 
0.8%
82.31
 
0.8%
82.22
1.5%
821
 
0.8%
81.72
1.5%

Adult Mortality
Real number (ℝ≥0)

HIGH CORRELATION

Distinct103
Distinct (%)78.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean160.3740458
Minimum2
Maximum522
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-10-15T21:14:57.744797image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile12
Q174.5
median144
Q3225
95-th percentile368.5
Maximum522
Range520
Interquartile range (IQR)150.5

Descriptive statistics

Standard deviation110.1423789
Coefficient of variation (CV)0.6867843132
Kurtosis0.3029280557
Mean160.3740458
Median Absolute Deviation (MAD)78
Skewness0.7557706801
Sum21009
Variance12131.34363
MonotonicityNot monotonic
2022-10-15T21:14:57.872644image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
173
 
2.3%
233
 
2.3%
2253
 
2.3%
123
 
2.3%
1172
 
1.5%
1792
 
1.5%
2172
 
1.5%
652
 
1.5%
3622
 
1.5%
582
 
1.5%
Other values (93)107
81.7%
ValueCountFrequency (%)
21
 
0.8%
62
1.5%
81
 
0.8%
111
 
0.8%
123
2.3%
173
2.3%
191
 
0.8%
211
 
0.8%
221
 
0.8%
233
2.3%
ValueCountFrequency (%)
5221
0.8%
4631
0.8%
4371
0.8%
3821
0.8%
3771
0.8%
3751
0.8%
3711
0.8%
3661
0.8%
3622
1.5%
3481
0.8%

infant deaths
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct49
Distinct (%)37.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.5648855
Minimum0
Maximum957
Zeros34
Zeros (%)26.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-10-15T21:14:58.003139image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q320
95-th percentile83
Maximum957
Range957
Interquartile range (IQR)20

Descriptive statistics

Standard deviation100.0954236
Coefficient of variation (CV)3.504142303
Kurtosis61.20962848
Mean28.5648855
Median Absolute Deviation (MAD)3
Skewness7.310780584
Sum3742
Variance10019.09383
MonotonicityNot monotonic
2022-10-15T21:14:58.128469image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
034
26.0%
115
 
11.5%
213
 
9.9%
36
 
4.6%
45
 
3.8%
85
 
3.8%
103
 
2.3%
112
 
1.5%
182
 
1.5%
272
 
1.5%
Other values (39)44
33.6%
ValueCountFrequency (%)
034
26.0%
115
11.5%
213
 
9.9%
36
 
4.6%
45
 
3.8%
52
 
1.5%
62
 
1.5%
71
 
0.8%
85
 
3.8%
103
 
2.3%
ValueCountFrequency (%)
9571
0.8%
4901
0.8%
3591
0.8%
1711
0.8%
1401
0.8%
1191
0.8%
981
0.8%
681
0.8%
671
0.8%
641
0.8%

Alcohol
Real number (ℝ≥0)

HIGH CORRELATION

Distinct65
Distinct (%)49.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.060916031
Minimum0.01
Maximum15.19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-10-15T21:14:58.256160image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.01
Q10.01
median0.01
Q36.305
95-th percentile11.31
Maximum15.19
Range15.18
Interquartile range (IQR)6.295

Descriptive statistics

Standard deviation4.090299115
Coefficient of variation (CV)1.336299027
Kurtosis-0.01274928092
Mean3.060916031
Median Absolute Deviation (MAD)0
Skewness1.090225337
Sum400.98
Variance16.73054685
MonotonicityNot monotonic
2022-10-15T21:14:58.381455image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0167
51.1%
2.621
 
0.8%
3.831
 
0.8%
0.411
 
0.8%
6.291
 
0.8%
1.321
 
0.8%
15.191
 
0.8%
11.121
 
0.8%
0.521
 
0.8%
8.491
 
0.8%
Other values (55)55
42.0%
ValueCountFrequency (%)
0.0167
51.1%
0.091
 
0.8%
0.261
 
0.8%
0.381
 
0.8%
0.411
 
0.8%
0.431
 
0.8%
0.521
 
0.8%
1.321
 
0.8%
1.391
 
0.8%
1.451
 
0.8%
ValueCountFrequency (%)
15.191
0.8%
13.941
0.8%
12.61
0.8%
12.321
0.8%
12.141
0.8%
12.031
0.8%
11.51
0.8%
11.121
0.8%
11.031
0.8%
10.751
0.8%

percentage expenditure
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct131
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean850.8741145
Minimum0.442802404
Maximum16255.16198
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-10-15T21:14:58.515432image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.442802404
5-th percentile4.990299788
Q148.31142608
median198.7343495
Q3718.3236185
95-th percentile3524.20418
Maximum16255.16198
Range16254.71918
Interquartile range (IQR)670.0121925

Descriptive statistics

Standard deviation2071.444348
Coefficient of variation (CV)2.434489795
Kurtosis28.60519673
Mean850.8741145
Median Absolute Deviation (MAD)190.1171411
Skewness4.919263564
Sum111464.509
Variance4290881.687
MonotonicityNot monotonic
2022-10-15T21:14:58.644748image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73.523581681
 
0.8%
271.25455311
 
0.8%
31.272321881
 
0.8%
973.7286751
 
0.8%
561.76847011
 
0.8%
208.23116031
 
0.8%
1842.4637821
 
0.8%
62.293610921
 
0.8%
263.21110311
 
0.8%
3.3040398991
 
0.8%
Other values (121)121
92.4%
ValueCountFrequency (%)
0.4428024041
0.8%
1.4432863531
0.8%
1.5764091721
0.8%
3.3040398991
0.8%
3.7184387991
0.8%
4.1412933451
0.8%
4.8773501391
0.8%
5.1032494381
0.8%
5.3390656731
0.8%
5.6638493281
0.8%
ValueCountFrequency (%)
16255.161981
0.8%
10769.363051
0.8%
8350.1935231
0.8%
7163.3489231
0.8%
6739.6776061
0.8%
4831.6447961
0.8%
4348.335311
0.8%
2700.073051
0.8%
2352.9995911
0.8%
2211.7441781
0.8%

Hepatitis B
Real number (ℝ≥0)

HIGH CORRELATION

Distinct40
Distinct (%)30.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.70992366
Minimum2
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-10-15T21:14:58.782401image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile9
Q178
median91
Q396
95-th percentile99
Maximum99
Range97
Interquartile range (IQR)18

Descriptive statistics

Standard deviation23.76406095
Coefficient of variation (CV)0.290834453
Kurtosis3.923599159
Mean81.70992366
Median Absolute Deviation (MAD)6
Skewness-2.129444744
Sum10704
Variance564.7305931
MonotonicityNot monotonic
2022-10-15T21:14:58.899948image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
9512
 
9.2%
9811
 
8.4%
9910
 
7.6%
979
 
6.9%
927
 
5.3%
967
 
5.3%
887
 
5.3%
916
 
4.6%
945
 
3.8%
875
 
3.8%
Other values (30)52
39.7%
ValueCountFrequency (%)
21
 
0.8%
51
 
0.8%
71
 
0.8%
83
2.3%
92
1.5%
221
 
0.8%
371
 
0.8%
471
 
0.8%
481
 
0.8%
491
 
0.8%
ValueCountFrequency (%)
9910
7.6%
9811
8.4%
979
6.9%
967
5.3%
9512
9.2%
945
3.8%
934
 
3.1%
927
5.3%
916
4.6%
893
 
2.3%

Measles
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct67
Distinct (%)51.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2042.862595
Minimum0
Maximum79563
Zeros53
Zeros (%)40.5%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-10-15T21:14:59.026605image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median10
Q3289.5
95-th percentile5783
Maximum79563
Range79563
Interquartile range (IQR)289.5

Descriptive statistics

Standard deviation9842.341166
Coefficient of variation (CV)4.817916383
Kurtosis42.52187457
Mean2042.862595
Median Absolute Deviation (MAD)10
Skewness6.400971771
Sum267615
Variance96871679.63
MonotonicityNot monotonic
2022-10-15T21:14:59.155750image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
053
40.5%
15
 
3.8%
104
 
3.1%
33
 
2.3%
92
 
1.5%
132
 
1.5%
332
 
1.5%
12791
 
0.8%
13701
 
0.8%
68551
 
0.8%
Other values (57)57
43.5%
ValueCountFrequency (%)
053
40.5%
15
 
3.8%
33
 
2.3%
61
 
0.8%
81
 
0.8%
92
 
1.5%
104
 
3.1%
111
 
0.8%
132
 
1.5%
141
 
0.8%
ValueCountFrequency (%)
795631
0.8%
588481
0.8%
526281
0.8%
129431
0.8%
127391
0.8%
116991
0.8%
68551
0.8%
47111
0.8%
31881
0.8%
30001
0.8%

BMI
Real number (ℝ≥0)

HIGH CORRELATION

Distinct107
Distinct (%)81.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.47557252
Minimum2
Maximum77.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-10-15T21:14:59.293305image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5.25
Q122.85
median45.9
Q359.45
95-th percentile66
Maximum77.1
Range75.1
Interquartile range (IQR)36.6

Descriptive statistics

Standard deviation20.73366676
Coefficient of variation (CV)0.5122513523
Kurtosis-1.294144458
Mean40.47557252
Median Absolute Deviation (MAD)17.5
Skewness-0.2258707733
Sum5302.3
Variance429.8849372
MonotonicityNot monotonic
2022-10-15T21:14:59.422654image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55.33
 
2.3%
61.93
 
2.3%
22.73
 
2.3%
63.13
 
2.3%
59.92
 
1.5%
53.52
 
1.5%
24.72
 
1.5%
6.92
 
1.5%
23.72
 
1.5%
662
 
1.5%
Other values (97)107
81.7%
ValueCountFrequency (%)
21
0.8%
2.81
0.8%
3.12
1.5%
3.51
0.8%
5.11
0.8%
5.21
0.8%
5.31
0.8%
6.21
0.8%
6.72
1.5%
6.92
1.5%
ValueCountFrequency (%)
77.11
0.8%
74.81
0.8%
74.31
0.8%
69.21
0.8%
66.41
0.8%
66.11
0.8%
662
1.5%
65.41
0.8%
65.31
0.8%
65.11
0.8%

under-five deaths
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct50
Distinct (%)38.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.23664122
Minimum0
Maximum1200
Zeros30
Zeros (%)22.9%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-10-15T21:14:59.557310image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q325.5
95-th percentile111
Maximum1200
Range1200
Interquartile range (IQR)24.5

Descriptive statistics

Standard deviation131.2930856
Coefficient of variation (CV)3.433698187
Kurtosis54.39985856
Mean38.23664122
Median Absolute Deviation (MAD)3
Skewness6.95629245
Sum5009
Variance17237.87434
MonotonicityNot monotonic
2022-10-15T21:14:59.685226image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
030
22.9%
118
 
13.7%
29
 
6.9%
39
 
6.9%
44
 
3.1%
63
 
2.3%
393
 
2.3%
123
 
2.3%
322
 
1.5%
132
 
1.5%
Other values (40)48
36.6%
ValueCountFrequency (%)
030
22.9%
118
13.7%
29
 
6.9%
39
 
6.9%
44
 
3.1%
52
 
1.5%
63
 
2.3%
72
 
1.5%
81
 
0.8%
91
 
0.8%
ValueCountFrequency (%)
12001
0.8%
7591
0.8%
4421
0.8%
2021
0.8%
1981
0.8%
1421
0.8%
1211
0.8%
1011
0.8%
971
0.8%
881
0.8%

Polio
Real number (ℝ≥0)

HIGH CORRELATION

Distinct38
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.49618321
Minimum8
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-10-15T21:14:59.816832image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile43
Q178
median92
Q397
95-th percentile99
Maximum99
Range91
Interquartile range (IQR)19

Descriptive statistics

Standard deviation20.96641052
Coefficient of variation (CV)0.2511062148
Kurtosis4.703556424
Mean83.49618321
Median Absolute Deviation (MAD)6
Skewness-2.181121297
Sum10938
Variance439.5903699
MonotonicityNot monotonic
2022-10-15T21:14:59.930582image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
9915
 
11.5%
9512
 
9.2%
9812
 
9.2%
978
 
6.1%
937
 
5.3%
946
 
4.6%
925
 
3.8%
965
 
3.8%
915
 
3.8%
875
 
3.8%
Other values (28)51
38.9%
ValueCountFrequency (%)
82
1.5%
93
2.3%
241
 
0.8%
421
 
0.8%
441
 
0.8%
451
 
0.8%
471
 
0.8%
492
1.5%
551
 
0.8%
582
1.5%
ValueCountFrequency (%)
9915
11.5%
9812
9.2%
978
6.1%
965
 
3.8%
9512
9.2%
946
 
4.6%
937
5.3%
925
 
3.8%
915
 
3.8%
883
 
2.3%

Total expenditure
Real number (ℝ≥0)

HIGH CORRELATION

Distinct124
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.106717557
Minimum1.21
Maximum13.73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-10-15T21:15:00.055138image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1.21
5-th percentile1.605
Q14.485
median5.82
Q37.63
95-th percentile10.51
Maximum13.73
Range12.52
Interquartile range (IQR)3.145

Descriptive statistics

Standard deviation2.533227325
Coefficient of variation (CV)0.4148263452
Kurtosis0.04509294338
Mean6.106717557
Median Absolute Deviation (MAD)1.62
Skewness0.3171580668
Sum799.98
Variance6.417240681
MonotonicityNot monotonic
2022-10-15T21:15:00.188676image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.222
 
1.5%
5.882
 
1.5%
5.412
 
1.5%
5.252
 
1.5%
9.252
 
1.5%
8.82
 
1.5%
5.692
 
1.5%
8.181
 
0.8%
5.911
 
0.8%
6.981
 
0.8%
Other values (114)114
87.0%
ValueCountFrequency (%)
1.211
0.8%
1.371
0.8%
1.41
0.8%
1.451
0.8%
1.481
0.8%
1.571
0.8%
1.591
0.8%
1.621
0.8%
1.91
0.8%
2.281
0.8%
ValueCountFrequency (%)
13.731
0.8%
11.931
0.8%
11.91
0.8%
11.541
0.8%
11.381
0.8%
11.31
0.8%
11.211
0.8%
9.811
0.8%
9.751
0.8%
9.571
0.8%

Diphtheria
Real number (ℝ≥0)

HIGH CORRELATION

Distinct39
Distinct (%)29.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.88549618
Minimum2
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-10-15T21:15:00.566147image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile30
Q180
median92
Q397
95-th percentile99
Maximum99
Range97
Interquartile range (IQR)17

Descriptive statistics

Standard deviation21.83985109
Coefficient of variation (CV)0.2603531252
Kurtosis5.57738669
Mean83.88549618
Median Absolute Deviation (MAD)6
Skewness-2.403467177
Sum10989
Variance476.9790957
MonotonicityNot monotonic
2022-10-15T21:15:00.682580image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
9915
 
11.5%
9814
 
10.7%
9513
 
9.9%
918
 
6.1%
978
 
6.1%
886
 
4.6%
876
 
4.6%
936
 
4.6%
926
 
4.6%
734
 
3.1%
Other values (29)45
34.4%
ValueCountFrequency (%)
21
0.8%
51
0.8%
71
0.8%
82
1.5%
91
0.8%
231
0.8%
371
0.8%
471
0.8%
481
0.8%
491
0.8%
ValueCountFrequency (%)
9915
11.5%
9814
10.7%
978
6.1%
963
 
2.3%
9513
9.9%
944
 
3.1%
936
 
4.6%
926
 
4.6%
918
6.1%
891
 
0.8%

HIV/AIDS
Real number (ℝ≥0)

HIGH CORRELATION

Distinct30
Distinct (%)22.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8099236641
Minimum0.1
Maximum9.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-10-15T21:15:00.796117image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.1
Q10.1
median0.1
Q30.5
95-th percentile4.2
Maximum9.4
Range9.3
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation1.562042793
Coefficient of variation (CV)1.92862965
Kurtosis9.757039882
Mean0.8099236641
Median Absolute Deviation (MAD)0
Skewness2.954731088
Sum106.1
Variance2.439977686
MonotonicityNot monotonic
2022-10-15T21:15:00.896827image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0.174
56.5%
0.29
 
6.9%
0.38
 
6.1%
0.55
 
3.8%
0.43
 
2.3%
0.63
 
2.3%
3.72
 
1.5%
0.82
 
1.5%
2.92
 
1.5%
0.72
 
1.5%
Other values (20)21
 
16.0%
ValueCountFrequency (%)
0.174
56.5%
0.29
 
6.9%
0.38
 
6.1%
0.43
 
2.3%
0.55
 
3.8%
0.63
 
2.3%
0.72
 
1.5%
0.82
 
1.5%
0.92
 
1.5%
11
 
0.8%
ValueCountFrequency (%)
9.41
0.8%
7.31
0.8%
6.31
0.8%
5.11
0.8%
4.51
0.8%
4.41
0.8%
4.31
0.8%
4.11
0.8%
3.91
0.8%
3.72
1.5%

GDP
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct131
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7256.846908
Minimum12.27733
Maximum119172.7418
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-10-15T21:15:01.010371image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum12.27733
5-th percentile68.65981615
Q1554.9245435
median2522.7968
Q37438.053832
95-th percentile36489.75571
Maximum119172.7418
Range119160.4645
Interquartile range (IQR)6883.129289

Descriptive statistics

Standard deviation14741.39573
Coefficient of variation (CV)2.031377528
Kurtosis27.59973911
Mean7256.846908
Median Absolute Deviation (MAD)2269.85608
Skewness4.622799206
Sum950646.9449
Variance217308748.2
MonotonicityNot monotonic
2022-10-15T21:15:01.142856image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
612.6965141
 
0.8%
2277.536131
 
0.8%
2842.9383531
 
0.8%
6491.52451
 
0.8%
4712.8227361
 
0.8%
2182.7165651
 
0.8%
12593.73741
 
0.8%
1316.989661
 
0.8%
3221.6781281
 
0.8%
43.6464981
 
0.8%
Other values (121)121
92.4%
ValueCountFrequency (%)
12.277331
0.8%
25.4484141
0.8%
29.6526221
0.8%
43.6464981
0.8%
43.823211
0.8%
62.13184891
0.8%
62.1732211
0.8%
75.14641131
0.8%
76.23869771
0.8%
76.56995171
0.8%
ValueCountFrequency (%)
119172.74181
0.8%
62214.69121
0.8%
52157.46871
0.8%
51322.639971
0.8%
47439.396841
0.8%
42955.242871
0.8%
37582.846241
0.8%
35396.665171
0.8%
21673.78171
0.8%
19941.455321
0.8%

Population
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct131
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22269096.43
Minimum41
Maximum1293859294
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-10-15T21:15:01.275933image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum41
5-th percentile17505.5
Q1287600.5
median1562974
Q38059265
95-th percentile60239345.87
Maximum1293859294
Range1293859253
Interquartile range (IQR)7771664.5

Descriptive statistics

Standard deviation116699866.4
Coefficient of variation (CV)5.240440121
Kurtosis110.6561006
Mean22269096.43
Median Absolute Deviation (MAD)1474038
Skewness10.19705964
Sum2917251633
Variance1.361885882 × 1016
MonotonicityNot monotonic
2022-10-15T21:15:01.409494image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3275821
 
0.8%
141621
 
0.8%
1122491
 
0.8%
39733541
 
0.8%
65525841
 
0.8%
77557851
 
0.8%
3939861
 
0.8%
1855462571
 
0.8%
17646521
 
0.8%
191482191
 
0.8%
Other values (121)121
92.4%
ValueCountFrequency (%)
411
0.8%
27711
0.8%
35681
0.8%
37271
0.8%
114581
0.8%
141621
0.8%
157821
0.8%
192291
0.8%
258851
0.8%
296221
0.8%
ValueCountFrequency (%)
12938592941
0.8%
2551311161
0.8%
1855462571
0.8%
1438196661
0.8%
973667741
0.8%
684167721
0.8%
663319571
0.8%
54146734.741
0.8%
519241821
0.8%
477919111
0.8%

thinness 1-19 years
Real number (ℝ≥0)

HIGH CORRELATION

Distinct77
Distinct (%)58.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.648091603
Minimum0.1
Maximum26.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-10-15T21:15:01.541507image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.6
Q11.5
median3.3
Q36.65
95-th percentile14.4
Maximum26.8
Range26.7
Interquartile range (IQR)5.15

Descriptive statistics

Standard deviation4.420587346
Coefficient of variation (CV)0.9510542656
Kurtosis5.674876594
Mean4.648091603
Median Absolute Deviation (MAD)2.3
Skewness2.036580536
Sum608.9
Variance19.54159248
MonotonicityNot monotonic
2022-10-15T21:15:01.673074image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.97
 
5.3%
15
 
3.8%
1.55
 
3.8%
1.14
 
3.1%
1.24
 
3.1%
2.14
 
3.1%
0.64
 
3.1%
1.84
 
3.1%
5.83
 
2.3%
0.83
 
2.3%
Other values (67)88
67.2%
ValueCountFrequency (%)
0.12
 
1.5%
0.21
 
0.8%
0.31
 
0.8%
0.51
 
0.8%
0.64
3.1%
0.71
 
0.8%
0.83
2.3%
0.91
 
0.8%
15
3.8%
1.14
3.1%
ValueCountFrequency (%)
26.81
0.8%
19.41
0.8%
18.11
0.8%
17.51
0.8%
15.91
0.8%
15.71
0.8%
15.21
0.8%
13.61
0.8%
12.91
0.8%
9.81
0.8%

thinness 5-9 years
Real number (ℝ≥0)

HIGH CORRELATION

Distinct75
Distinct (%)57.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.886259542
Minimum0.1
Maximum27.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-10-15T21:15:01.804555image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.55
Q11.55
median3.5
Q36.8
95-th percentile14.35
Maximum27.4
Range27.3
Interquartile range (IQR)5.25

Descriptive statistics

Standard deviation4.543478223
Coefficient of variation (CV)0.92984791
Kurtosis5.217264438
Mean4.886259542
Median Absolute Deviation (MAD)2.4
Skewness1.92762743
Sum640.1
Variance20.64319436
MonotonicityNot monotonic
2022-10-15T21:15:01.926867image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.95
 
3.8%
1.14
 
3.1%
7.74
 
3.1%
2.14
 
3.1%
1.34
 
3.1%
1.23
 
2.3%
7.43
 
2.3%
0.53
 
2.3%
63
 
2.3%
6.43
 
2.3%
Other values (65)95
72.5%
ValueCountFrequency (%)
0.13
2.3%
0.21
 
0.8%
0.53
2.3%
0.63
2.3%
0.71
 
0.8%
0.82
1.5%
0.93
2.3%
12
1.5%
1.14
3.1%
1.23
2.3%
ValueCountFrequency (%)
27.41
0.8%
19.81
0.8%
18.61
0.8%
17.51
0.8%
16.31
0.8%
16.21
0.8%
151
0.8%
13.71
0.8%
13.11
0.8%
11.11
0.8%

Income composition of resources
Real number (ℝ≥0)

HIGH CORRELATION

Distinct111
Distinct (%)84.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6696870229
Minimum0.345
Maximum0.936
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-10-15T21:15:02.057183image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.345
5-th percentile0.4155
Q10.544
median0.697
Q30.779
95-th percentile0.8935
Maximum0.936
Range0.591
Interquartile range (IQR)0.235

Descriptive statistics

Standard deviation0.1513143485
Coefficient of variation (CV)0.2259478582
Kurtosis-0.9419839777
Mean0.6696870229
Median Absolute Deviation (MAD)0.115
Skewness-0.2417573244
Sum87.729
Variance0.02289603206
MonotonicityNot monotonic
2022-10-15T21:15:02.182144image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.7373
 
2.3%
0.6362
 
1.5%
0.4752
 
1.5%
0.852
 
1.5%
0.7272
 
1.5%
0.7592
 
1.5%
0.8412
 
1.5%
0.3452
 
1.5%
0.4832
 
1.5%
0.8032
 
1.5%
Other values (101)110
84.0%
ValueCountFrequency (%)
0.3452
1.5%
0.391
0.8%
0.3981
0.8%
0.4041
0.8%
0.4091
0.8%
0.4121
0.8%
0.4191
0.8%
0.4262
1.5%
0.431
0.8%
0.4351
0.8%
ValueCountFrequency (%)
0.9361
0.8%
0.9231
0.8%
0.921
0.8%
0.9121
0.8%
0.911
0.8%
0.9061
0.8%
0.8951
0.8%
0.8922
1.5%
0.892
1.5%
0.8772
1.5%

Schooling
Real number (ℝ≥0)

HIGH CORRELATION

Distinct75
Distinct (%)57.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.67633588
Minimum5.3
Maximum20.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-10-15T21:15:02.316220image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum5.3
5-th percentile8.3
Q110.75
median12.7
Q314.7
95-th percentile16.9
Maximum20.4
Range15.1
Interquartile range (IQR)3.95

Descriptive statistics

Standard deviation2.750379892
Coefficient of variation (CV)0.2169696289
Kurtosis-0.1384092071
Mean12.67633588
Median Absolute Deviation (MAD)2
Skewness-0.03599040835
Sum1660.6
Variance7.564589548
MonotonicityNot monotonic
2022-10-15T21:15:02.444066image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105
 
3.8%
12.74
 
3.1%
10.84
 
3.1%
9.14
 
3.1%
15.23
 
2.3%
14.73
 
2.3%
14.33
 
2.3%
133
 
2.3%
10.33
 
2.3%
13.23
 
2.3%
Other values (65)96
73.3%
ValueCountFrequency (%)
5.31
0.8%
6.31
0.8%
7.11
0.8%
7.31
0.8%
7.71
0.8%
7.81
0.8%
8.21
0.8%
8.41
0.8%
8.51
0.8%
8.61
0.8%
ValueCountFrequency (%)
20.41
0.8%
18.61
0.8%
18.11
0.8%
17.61
0.8%
17.31
0.8%
17.21
0.8%
171
0.8%
16.81
0.8%
16.52
1.5%
16.41
0.8%

Interactions

2022-10-15T21:14:53.632029image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:15.036597image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:17.222726image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:19.440734image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:21.290789image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:23.414323image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:25.315023image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:27.563514image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:29.406865image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:31.674268image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:33.885529image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:35.779690image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:37.783697image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:39.651796image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:41.729457image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:43.765808image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:45.642576image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:47.771331image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:49.613600image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:51.721168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:53.725901image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:15.189900image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:17.320714image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:19.531883image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:21.386331image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:23.506577image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:25.412885image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:27.653942image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:29.510293image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:31.773689image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:33.978369image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:35.868452image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:37.876036image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:39.740686image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:41.817200image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:43.857217image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:45.739392image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:47.861998image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:49.708607image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:51.815747image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:53.827345image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:15.293982image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:17.425446image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:19.631477image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:21.491613image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:23.606562image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:25.518249image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:27.753415image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:29.887698image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:31.882839image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:34.078635image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:35.963361image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:37.976594image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:39.837494image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:41.913824image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:43.958190image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:45.841054image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:47.959987image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:49.808068image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:51.916316image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:54.180822image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:15.392739image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:17.519511image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:19.719498image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:21.590120image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:23.697246image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:25.612434image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:27.841439image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:29.983879image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:31.977293image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:34.171256image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:36.046883image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:38.065505image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:39.925450image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:42.005079image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:44.050747image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:45.931334image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:48.046312image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:49.896287image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:52.006203image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:54.272677image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:15.483113image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:17.613700image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:19.808564image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:21.678874image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:23.786328image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:25.709351image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:27.929469image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:30.083130image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:32.070687image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:34.262560image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:36.131440image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:38.155466image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:40.275407image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:42.094905image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:44.140732image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:46.021469image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:48.133909image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:49.984242image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:52.099132image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:54.368251image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:15.580358image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:17.719317image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:19.903602image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:21.771119image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:23.883930image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:25.810849image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:28.023920image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:30.187347image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:32.171086image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:34.357459image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:36.221076image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:38.250005image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:40.367146image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:42.195048image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:44.236790image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:46.117189image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:48.228878image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:50.077448image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:52.196366image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:54.470982image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:15.687781image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:17.825885image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:20.006397image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:21.868433image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:23.984803image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:25.916076image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:28.124061image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:30.293450image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:32.275800image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:34.459035image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:36.315480image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:38.350399image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:40.465138image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:42.292224image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:44.339061image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:46.218969image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:48.326627image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:50.177672image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:52.302621image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:54.568913image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:16.031127image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:17.919978image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:20.093731image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-10-15T21:14:25.035903image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:27.264221image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:29.133812image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:31.373681image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:33.590959image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:35.494596image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:37.517440image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:39.373989image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:41.457546image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:43.253847image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:45.359311image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:47.488726image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:49.324564image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:51.439698image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:53.354018image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:55.615130image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:17.035468image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:18.994036image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:21.103201image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:23.200216image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:25.127236image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:27.362479image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:29.221719image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:31.469116image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:33.686684image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:35.585677image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:37.604230image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:39.465062image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:41.546409image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:43.340489image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:45.449796image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:47.580551image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:49.421262image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:51.530579image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:53.444473image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:55.711176image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:17.128261image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:19.094141image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:21.194651image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:23.292952image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:25.219854image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:27.461404image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:29.313004image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:31.569306image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:33.784176image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:35.681055image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:37.693693image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:39.557584image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:41.636603image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:43.676236image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:45.543761image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:47.675470image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:49.518194image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:51.624494image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-15T21:14:53.536380image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-10-15T21:15:02.592872image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-15T21:15:02.848976image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-15T21:15:03.090416image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-15T21:15:03.566579image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-10-15T21:15:03.689012image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-15T21:14:55.903481image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-15T21:14:56.289092image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexCountryYearStatusLife expectancyAdult Mortalityinfant deathsAlcoholpercentage expenditureHepatitis BMeaslesBMIunder-five deathsPolioTotal expenditureDiphtheriaHIV/AIDSGDPPopulationthinness 1-19 yearsthinness 5-9 yearsIncome composition of resourcesSchooling
01Afghanistan2014Developing59.9271.0640.0173.52358262.049218.68658.08.1862.00.1612.696514327582.017.517.50.47610.0
117Albania2014Developing77.58.004.51428.74906798.0057.2198.05.8898.00.14575.763787288914.01.21.30.76114.2
233Algeria2014Developing75.411.0210.0154.23731895.0058.42495.07.2195.00.1547.85170039113313.06.05.80.74114.4
349Angola2014Developing51.7348.0678.3323.96561264.01169922.710168.03.3164.02.0479.3122402692466.08.58.30.52711.4
481Argentina2014Developing76.2118.087.93847.37174694.0162.2992.04.7994.00.112245.25645042981515.01.00.90.82517.3
597Armenia2014Developing74.612.013.91295.60871493.01354.1195.04.4893.00.13994.71235529622.02.12.10.73912.7
6113Australia2014Developed82.76.019.7110769.36305091.034066.1192.09.4292.00.162214.6912002346694.00.60.60.93620.4
7129Austria2014Developed81.466.0012.328350.19352398.011757.1098.011.2198.00.151322.6399708541575.01.82.00.89215.9
8145Azerbaijan2014Developing72.5119.050.01306.18243194.0051.5697.06.4094.00.17891.299776953579.02.82.90.75212.2
9193Bangladesh2014Developing71.4132.0980.0110.44640397.028917.712197.02.8297.00.1184.56543015945279.018.118.60.57010.0

Last rows

df_indexCountryYearStatusLife expectancyAdult Mortalityinfant deathsAlcoholpercentage expenditureHepatitis BMeaslesBMIunder-five deathsPolioTotal expenditureDiphtheriaHIV/AIDSGDPPopulationthinness 1-19 yearsthinness 5-9 yearsIncome composition of resourcesSchooling
1212666Tunisia2014Developing75.112.031.39604.87013298.0156.2398.07.0098.00.14271.6817201114398.06.56.40.72214.7
1222682Turkey2014Developing75.517.0161.45181.90837896.056565.31996.05.4196.00.112127.225220773628.04.94.70.75914.5
1232698Turkmenistan2014Developing66.0217.072.90691.13335397.0047.7898.02.7098.00.17962.3658245466241.03.33.30.68310.8
1242715Uganda2014Developing61.538.0680.0114.16770278.031418.19782.07.2278.03.2719.17266938833338.05.75.60.48310.0
1252731Ukraine2014Developing78.023.048.065.66384922.006.7545.07.1023.00.2314.65829645271947.02.32.40.74615.2
1262811Uruguay2014Developing76.8117.006.03463.63978295.0063.4095.08.5895.00.116737.8982703419546.01.51.40.79115.5
1272827Uzbekistan2014Developing69.2184.0160.010.44280299.0843.91899.05.8499.00.125.44841437577.03.03.10.69012.1
1282843Vanuatu2014Developing71.7134.000.01564.81670464.01052.5065.05.2064.00.13148.36513025885.01.51.40.59610.8
1292907Zambia2014Developing61.1314.0280.01196.66757786.0922.84178.04.9986.04.31738.8822001562974.06.36.20.57012.5
1302923Zimbabwe2014Developing59.2371.0236.5010.82259591.0031.33492.06.4491.06.3127.47462015411675.05.95.70.49810.3